├── .gitignore ├── .nojekyll ├── 404.html ├── CONTRIBUTING.md ├── Dockerfile ├── LICENSE ├── README.md ├── SUMMARY.md ├── asset ├── docsify-apachecn-footer.js ├── docsify-baidu-push.js ├── docsify-baidu-stat.js ├── docsify-clicker.js ├── docsify-cnzz.js ├── docsify-copy-code.min.js ├── docsify.min.js ├── prism-darcula.css ├── prism-python.min.js ├── search.min.js ├── style.css └── vue.css ├── docs ├── 1.md ├── 10.md ├── 11.md ├── 12.md ├── 13.md ├── 14.md ├── 15.md ├── 16.md ├── 17.md ├── 18.md ├── 19.md ├── 2.md ├── 20.md ├── 21.md ├── 22.md ├── 23.md ├── 24.md ├── 25.md ├── 26.md ├── 27.md ├── 28.md ├── 29.md ├── 3.md ├── 30.md ├── 31.md ├── 32.md ├── 33.md ├── 34.md ├── 35.md ├── 36.md ├── 37.md ├── 38.md ├── 39.md ├── 4.md ├── 40.md ├── 41.md ├── 42.md ├── 43.md ├── 44.md ├── 45.md ├── 46.md ├── 47.md ├── 48.md ├── 49.md ├── 5.md ├── 50.md ├── 51.md ├── 52.md ├── 53.md ├── 54.md ├── 55.md ├── 56.md ├── 57.md ├── 58.md ├── 59.md ├── 6.md ├── 60.md ├── 61.md ├── 62.md ├── 63.md ├── 64.md ├── 65.md ├── 66.md ├── 67.md ├── 68.md ├── 69.md ├── 7.md ├── 70.md ├── 71.md ├── 72.md ├── 73.md ├── 74.md ├── 8.md ├── 9.md └── img │ ├── 0140b6849cae4a202c9a56ad5edfffd6.jpg │ ├── 02113b4a6b876c8e3b32dd2bb7eae74c.jpg │ ├── 05782b02c2faaf09a504b53a5a526445.jpg │ ├── 05a2ee4e2b81712775628943c2135066.jpg │ ├── 05cf1fb72d609a18fa7a4fdfb8ae460f.jpg │ ├── 06f05fb132ef0dc7f9cca60a16b84f1a.jpg │ ├── 077b00661754439549e60127485a6a4a.jpg │ ├── 09cb7121a5be805931cc8ae1c99d3cf9.jpg │ ├── 0a27825dabd4d46b78291be90e8f255e.jpg │ ├── 0a3f41c0a016c66f0f3379c128f550b9.jpg │ ├── 0a53d5e52e4ce61295a6c2b5fc4bf6c8.jpg │ ├── 0aa3ba1dbc0e103e4d7b96c4de278ec9.jpg │ ├── 0b12363978e10369fc98bd33cb536d85.jpg │ ├── 0b688ef9f59240384ca84839d9707ce7.jpg │ ├── 0c0c7dd9c7bdb6a6490d4f09db142ec7.jpg │ ├── 0d8364e9bb731fa5d89b998087ba0635.jpg │ ├── 0dc6d842f7d01eca73fa0b7426a6d152.jpg │ ├── 0ec9398faa407996527db66db46c71f2.jpg │ ├── 0fa41716b87d635876d921fc9ab967ea.jpg │ ├── 11fc790735a7bdc59ac4a30d6af7e6d2.jpg 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1e7ac5787ede28760da23610a7f3926f.jpg │ ├── 1eb024fe4ee82e1fd71c47c29ebf1856.jpg │ ├── 1f4ad95dbd00806b549ece0b87386cf9.jpg │ ├── 20114eb58ca40a3ccf0b20f14f426c83.jpg │ ├── 2017a8a703bdcc077baea030a758721d.jpg │ ├── 21df9f9baaa60a010973e5b26a1e7259.jpg │ ├── 23f314e72f6e5a8952dc9e9ad7e91951.jpg │ ├── 2422a0c0d2c96ec6397babaa6c842d79.jpg │ ├── 247df80468d3edbc28836cb1cc56c81c.jpg │ ├── 25659857727e22d0f951f9c750bb29de.jpg │ ├── 25ad91543cb2ac105ccfb3efc544658a.jpg │ ├── 2622373fb99932aa42e45c3b151135be.jpg │ ├── 2690d286a343ff53a43d15d54fc86e91.jpg │ ├── 26ac3dbbb98ec93352f776de6e6d2b65.jpg │ ├── 26d08ab02c44cb0a819065f27a1b7f84.jpg │ ├── 2749fd423c61cc0419daeeec8d8aa467.jpg │ ├── 2777268c97b2e7b9d1e8269eb5a30c32.jpg │ ├── 27d73e1a33e6845f05c2a4670413c156.jpg │ ├── 2aca4924009e92a55bc1579fc086d36c.jpg │ ├── 2b0ae6898e6a1091d872587335b2e5bf.jpg │ ├── 2b4711757bd351625d2244392491e1cf.jpg │ ├── 2b8879fda28a3be3acac977e15cbeebb.jpg │ ├── 2d47b5f96f08a539147e7f0a71d0aa33.jpg │ ├── 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fb3de8051b91bb0be2143717f96c0a7c.jpg │ ├── fb741f69016b77f7e6a93fc12ad8420c.jpg │ ├── fcc8ab8bf79674eb14cd31eead15dfd1.jpg │ ├── fce6798715088ab4d9f615ae89a67b2c.jpg │ ├── fce8e5a984297be3f6f8b5d4c5369a78.jpg │ ├── fd8b7fa16dccb291fe1a2148a45e3eba.jpg │ ├── fdcac2831e87bea8b0a208095eb15adc.jpg │ ├── fde16964145ae355ddbbeae37728260a.jpg │ ├── fe7b4873c67199b180714300c4b65def.jpg │ ├── fe9b97ad8c3e66c3a2514249fcd62ee6.jpg │ ├── fea324aca2ed4416872749b8352a5412.jpg │ ├── ff09acb1a4ecf2c617aa1323f8894fe5.jpg │ ├── ff165b205839381bf1fa11c2df3d176d.jpg │ ├── ff513e6aaa65c95e4855ad207cee0757.jpg │ └── ff58dc3588f11a9e3f9291926a0c86ff.jpg ├── index.html └── update.sh /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | .DS_Store 103 | 104 | # gitbook 105 | _book 106 | 107 | # node.js 108 | node_modules 109 | 110 | # windows 111 | Thumbs.db 112 | 113 | # word 114 | ~$*.docx 115 | ~$*.doc 116 | -------------------------------------------------------------------------------- /.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/seaborn-doc-zh/ddaa694e3bebcdec3c583b7b5b1f85e9f55cf82b/.nojekyll -------------------------------------------------------------------------------- /404.html: -------------------------------------------------------------------------------- 1 | --- 2 | permalink: /404.html 3 | --- 4 | 5 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM httpd:2.4 2 | COPY ./ /usr/local/apache2/htdocs/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # seaborn 0.9 中文文档 2 | 3 | > 原文：[seaborn: statistical data visualization](http://seaborn.pydata.org/) 4 | > 5 | > 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) 6 | > 7 | > 过早优化是万恶之源。——高德纳 8 | 9 | * [在线阅读](https://apachecn.github.io/seaborn-doc-zh) 10 | * [在线阅读（Gitee）](https://apachecn.gitee.io/seaborn-doc-zh/) 11 | * [ApacheCN 机器学习交流群 629470233](http://shang.qq.com/wpa/qunwpa?idkey=30e5f1123a79867570f665aa3a483ca404b1c3f77737bc01ec520ed5f078ddef) 12 | * [ApacheCN 学习资源](http://www.apachecn.org/) 13 | 14 | ## 联系方式 15 | 16 | ### 负责人 17 | 18 | + [飞龙](https://github.com/wizardforcel)：562826179 19 | 20 | ### 其他 21 | 22 | * 在我们的 [apachecn/seaborn-doc-zh](https://github.com/apachecn/seaborn-doc-zh) github 上提 issue. 23 | * 发邮件到 Email: `apachecn@163.com`. 24 | * 在我们的 [组织学习交流群](http://www.apachecn.org/organization/348.html) 中联系群主/管理员即可. 25 | 26 | ## 下载 27 | 28 | ### Docker 29 | 30 | ``` 31 | docker pull apachecn0/seaborn-doc-zh 32 | docker run -tid -p :80 apachecn0/seaborn-doc-zh 33 | # 访问 http://localhost:{port} 查看文档 34 | ``` 35 | 36 | ### PYPI 37 | 38 | ``` 39 | pip install seaborn-doc-zh 40 | seaborn-doc-zh 41 | # 访问 http://localhost:{port} 查看文档 42 | ``` 43 | 44 | ### NPM 45 | 46 | ``` 47 | npm install -g seaborn-doc-zh 48 | seaborn-doc-zh 49 | # 访问 http://localhost:{port} 查看文档 50 | ``` 51 | 52 | ## 赞助我们 53 | 54 | ![](http://data.apachecn.org/img/about/donate.jpg) 55 | -------------------------------------------------------------------------------- /SUMMARY.md: -------------------------------------------------------------------------------- 1 | + [seaborn 0.9 中文文档](README.md) 2 | + [Seaborn 简介](docs/1.md) 3 | + [安装和入门](docs/2.md) 4 | + [可视化统计关系](docs/3.md) 5 | + [可视化分类数据](docs/4.md) 6 | + [可视化数据集的分布](docs/5.md) 7 | + [线性关系可视化](docs/6.md) 8 | + [构建结构化多图网格](docs/7.md) 9 | + [控制图像的美学样式](docs/8.md) 10 | + [选择调色板](docs/9.md) 11 | + [seaborn.relplot](docs/10.md) 12 | + [seaborn.scatterplot](docs/11.md) 13 | + [seaborn.lineplot](docs/12.md) 14 | + [seaborn.catplot](docs/13.md) 15 | + [seaborn.stripplot](docs/14.md) 16 | + [seaborn.swarmplot](docs/15.md) 17 | + [seaborn.boxplot](docs/16.md) 18 | + [seaborn.violinplot](docs/17.md) 19 | + [seaborn.boxenplot](docs/18.md) 20 | + [seaborn.pointplot](docs/19.md) 21 | + [seaborn.barplot](docs/20.md) 22 | + [seaborn.countplot](docs/21.md) 23 | + [seaborn.jointplot](docs/22.md) 24 | + [seaborn.pairplot](docs/23.md) 25 | + [seaborn.distplot](docs/24.md) 26 | + [seaborn.kdeplot](docs/25.md) 27 | + [seaborn.rugplot](docs/26.md) 28 | + [seaborn.lmplot](docs/27.md) 29 | + [seaborn.regplot](docs/28.md) 30 | + [seaborn.residplot](docs/29.md) 31 | + [seaborn.heatmap](docs/30.md) 32 | + [seaborn.clustermap](docs/31.md) 33 | + [seaborn.FacetGrid](docs/32.md) 34 | + [seaborn.FacetGrid.map](docs/33.md) 35 | + [seaborn.FacetGrid.map_dataframe](docs/34.md) 36 | + [seaborn.PairGrid](docs/35.md) 37 | + [seaborn.PairGrid.map](docs/36.md) 38 | + [seaborn.PairGrid.map_diag](docs/37.md) 39 | + [seaborn.PairGrid.map_offdiag](docs/38.md) 40 | + [seaborn.PairGrid.map_lower](docs/39.md) 41 | + [seaborn.PairGrid.map_upper](docs/40.md) 42 | + [seaborn.JointGrid](docs/41.md) 43 | + [seaborn.JointGrid.plot](docs/42.md) 44 | + [seaborn.JointGrid.plot_joint](docs/43.md) 45 | + [seaborn.JointGrid.plot_marginals](docs/44.md) 46 | + [seaborn.set](docs/45.md) 47 | + [seaborn.axes_style](docs/46.md) 48 | + [seaborn.set_style](docs/47.md) 49 | + [seaborn.plotting_context](docs/48.md) 50 | + [seaborn.set_context](docs/49.md) 51 | + [seaborn.set_color_codes](docs/50.md) 52 | + [seaborn.reset_defaults](docs/51.md) 53 | + [seaborn.reset_orig](docs/52.md) 54 | + [seaborn.set_palette](docs/53.md) 55 | + [seaborn.color_palette](docs/54.md) 56 | + [seaborn.husl_palette](docs/55.md) 57 | + [seaborn.hls_palette](docs/56.md) 58 | + [seaborn.cubehelix_palette](docs/57.md) 59 | + [seaborn.dark_palette](docs/58.md) 60 | + [seaborn.light_palette](docs/59.md) 61 | + [seaborn.diverging_palette](docs/60.md) 62 | + [seaborn.blend_palette](docs/61.md) 63 | + [seaborn.xkcd_palette](docs/62.md) 64 | + [seaborn.crayon_palette](docs/63.md) 65 | + [seaborn.mpl_palette](docs/64.md) 66 | + [seaborn.choose_colorbrewer_palette](docs/65.md) 67 | + [seaborn.choose_cubehelix_palette](docs/66.md) 68 | + [seaborn.choose_light_palette](docs/67.md) 69 | + 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-------------------------------------------------------------------------------- /asset/style.css: -------------------------------------------------------------------------------- 1 | /*隐藏头部的目录*/ 2 | #main>ul:nth-child(1) { 3 | display: none; 4 | } 5 | 6 | #main>ul:nth-child(2) { 7 | display: none; 8 | } 9 | 10 | .markdown-section h1 { 11 | margin: 3rem 0 2rem 0; 12 | } 13 | 14 | .markdown-section h2 { 15 | margin: 2rem 0 1rem; 16 | } 17 | 18 | img, 19 | pre { 20 | border-radius: 8px; 21 | } 22 | 23 | .content, 24 | .sidebar, 25 | .markdown-section, 26 | body, 27 | .search input { 28 | background-color: rgba(243, 242, 238, 1) !important; 29 | } 30 | 31 | @media (min-width:600px) { 32 | .sidebar-toggle { 33 | background-color: #f3f2ee; 34 | } 35 | } 36 | 37 | .docsify-copy-code-button { 38 | background: #f8f8f8 !important; 39 | color: #7a7a7a !important; 40 | } 41 | 42 | body { 43 | /*font-family: Microsoft YaHei, Source Sans Pro, Helvetica Neue, Arial, sans-serif !important;*/ 44 | } 45 | 46 | .markdown-section>p { 47 | font-size: 16px !important; 48 | } 49 | 50 | .markdown-section pre>code { 51 | font-family: Consolas, Roboto Mono, Monaco, courier, monospace !important; 52 | font-size: .9rem !important; 53 | 54 | } 55 | 56 | /*.anchor span { 57 | color: rgb(66, 185, 131); 58 | }*/ 59 | 60 | section.cover h1 { 61 | margin: 0; 62 | } 63 | 64 | body>section>div.cover-main>ul>li>a { 65 | color: #42b983; 66 | } 67 | 68 | .markdown-section img { 69 | box-shadow: 7px 9px 10px #aaa !important; 70 | } 71 | 72 | 73 | pre { 74 | background-color: #f3f2ee !important; 75 | } 76 | 77 | @media (min-width:600px) { 78 | pre code { 79 | /*box-shadow: 2px 1px 20px 2px #aaa;*/ 80 | /*border-radius: 10px !important;*/ 81 | padding-left: 20px !important; 82 | } 83 | } 84 | 85 | @media (max-width:600px) { 86 | pre { 87 | padding-left: 0px !important; 88 | padding-right: 0px !important; 89 | } 90 | } 91 | 92 | .markdown-section pre { 93 | padding-left: 0 !important; 94 | padding-right: 0px !important; 95 | box-shadow: 2px 1px 20px 2px #aaa; 96 | } -------------------------------------------------------------------------------- /docs/2.md: -------------------------------------------------------------------------------- 1 | # 安装和入门 2 | 3 | > 译者：[Neolei](https://github.com/neolei) 4 | 5 | 为了安装最新版本的 seaborn, 可以 `pip`命令: 6 | 7 | ```py 8 | pip install seaborn 9 | ``` 10 | 11 | 也可以使用 `conda` 命令安装: 12 | 13 | ```py 14 | conda install seaborn 15 | ``` 16 | 17 | 或者，您可以使用 `pip` 直接从 github 安装开发版本: 18 | 19 | ```py 20 | pip install git+https://github.com/mwaskom/seaborn.git 21 | ``` 22 | 23 | 另外的方法是从 [github 仓库](https://github.com/mwaskom/seaborn) 下载，从本地安装: 24 | 25 | ```py 26 | pip install . 27 | ``` 28 | 29 | ## 依赖 30 | 31 | * Python 2.7 or 3.5+ 32 | 33 | ### 必须的依赖 34 | 35 | * [numpy](http://www.numpy.org/) (>= 1.9.3) 36 | * [scipy](https://www.scipy.org/) (>= 0.14.0) 37 | * [matplotlib](https://matplotlib.org) (>= 1.4.3) 38 | * [pandas](https://pandas.pydata.org/) (>= 0.15.2) 39 | 40 | ### 推荐的依赖 41 | 42 | * [statsmodels](https://www.statsmodels.org/) (>= 0.5.0) 43 | 44 | ## 测试 45 | 46 | 为了测试 seaborn，请在源代码分发的根目录中运行 `make test`。 这会运行单元测试套件（使用 `pytest`，但许多旧测试使用 `nose` 断言）。 它还在函数 docstrings 中运行示例代码，以对更广泛和更现实的示例用法进行冒烟测试。 47 | 48 | 完整的测试集需要 Internet 连接才能下载示例数据集（如果以前没有缓存过），但单元测试应该可以离线运行。 49 | 50 | ## 错误 51 | 52 | 请通过 github [issue tracker](https://github.com/mwaskom/seaborn/issues/new) 报告您遇到的任何错误. 在其中一个示例数据集中包含可重现的示例（通过 [`load_dataset()`](generated/seaborn.load_dataset.html#seaborn.load_dataset "seaborn.load_dataset") 访问）将是最有帮助的。 如果不知道你正在使用的 seaborn 和 matplotlib 的版本，以及 用于绘图的 [matplotlib backend](https://matplotlib.org/faq/usage_faq.html#what-is-a-backend)，你很难调试任何问题，所以请在错误报告中包含这些内容。 53 | -------------------------------------------------------------------------------- /docs/26.md: -------------------------------------------------------------------------------- 1 | # seaborn.rugplot 2 | 3 | > 译者：[P3n9W31](https://github.com/P3n9W31) 4 | 5 | ```py 6 | seaborn.rugplot(a, height=0.05, axis='x', ax=None, **kwargs) 7 | ``` 8 | 9 | 将数组中的数据点绘制为轴上的棒状标识。 10 | 11 | 参数：`a`：向量 12 | 13 | > 1 维的观察数组。 14 | 15 | `height`：标量, 可选 16 | 17 | > 以比例形式表示的坐标轴上棒状标识的高度。 18 | 19 | `axis`：{‘x’ | ‘y’}, 可选 20 | 21 | > 需要画 rugplot 的坐标轴 22 | 23 | `ax`：matplotlib 轴, 可选 24 | 25 | > 进行绘制的坐标轴; 未指定的话设定为当前轴。 26 | 27 | `kwargs`：键值对 28 | 29 | > 被传递给`LineCollection`的其他关键字参数。 30 | 31 | 32 | 返回值：`ax`：matplotlib Axex 对象 33 | 34 | > 在其上进行绘图的 Axex 对象。 35 | 36 | -------------------------------------------------------------------------------- /docs/29.md: -------------------------------------------------------------------------------- 1 | # seaborn.residplot 2 | 3 | > 译者：[P3n9W31](https://github.com/P3n9W31) 4 | 5 | ```py 6 | seaborn.residplot(x, y, data=None, lowess=False, x_partial=None, y_partial=None, order=1, robust=False, dropna=True, label=None, color=None, scatter_kws=None, line_kws=None, ax=None) 7 | ``` 8 | 9 | 绘制线性回归的残差。 10 | 11 | 此函数将在 x 上回归 y（可能作为鲁棒或多项式回归），然后绘制残差的散点图。 你可以选择将局部加权回归散点平滑法（LOWESS）拟合到残差图，这有助于确定残差是否存在结构。 12 | 13 | 参数：`x`： 向量或字符串 14 | 15 | > 预测变量数据中的数据或列名称。 16 | 17 | `y`：向量或字符串 18 | 19 | > 响应变量的数据中的数据或列名称。 20 | 21 | `data`：DataFrame, 可选 22 | 23 | > 如果 *x* 和 *y* 是列名，则指定使用的 DataFrame 24 | 25 | `lowess`： 布尔值, 可选 26 | 27 | > 将局部加权回归散点平滑法（LOWESS）应用到残差散点图中。 28 | 29 | `{x, y}_partial`：矩阵或字符串，可选 30 | 31 | > 具有与 x 相同的第一维的矩阵或数据中的列名称。这些变量被视为有误的，并在绘制之前从 x 或 y 变量中删除。 32 | 33 | `order`：整数，可选 34 | 35 | > 计算残差时拟合多项式的阶数。 36 | 37 | `robust`：布尔值，可选 38 | 39 | > 在计算残差时拟合稳健的线性回归。 40 | 41 | `dropna`：布尔值，可选 42 | 43 | > 如果为 True，则在拟合和绘图时忽略缺少的数据。 44 | 45 | `label`：字符串，可选 46 | 47 | > 将在任何图的图例中使用的标签。 48 | 49 | `color`：matplotlib 颜色，可选 50 | 51 | > 用于绘图的所有元素的颜色。 52 | 53 | `{scatter, line}_kws`： 字典，可选 54 | 55 | > 用于绘制图像的组件而传递给 scatter() 和 plot() 的其他关键字参数。 56 | 57 | `ax`：matplotlib 轴，可选 58 | 59 | > 绘制到指定轴对象，否则在当前轴对象上绘图，如果轴不存在则创建一个新轴。 60 | 61 | 62 | 返回值：ax：matplotlib Axes 对象 63 | 64 | > 带有回归图像的轴对象 65 | 66 | 67 | 68 | 也可以看看 69 | 70 | [`regplot`](http://seaborn.pydata.org/generated/seaborn.regplot.html#seaborn.regplot) 71 | 72 | 73 | 74 | 绘制一个简单的线性回归模型 75 | 76 | [`jointplot`](http://seaborn.pydata.org/generated/seaborn.jointplot.html#seaborn.jointplot) 77 | 78 | 边际分布。 -------------------------------------------------------------------------------- /docs/33.md: -------------------------------------------------------------------------------- 1 | # seaborn.FacetGrid.map 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | FacetGrid.map(func, *args, **kwargs) 7 | ``` 8 | 9 | 将绘图函数应用于每个方面的数据子集。 10 | 11 | 参数：`func`：可调用 12 | 13 | > 一个接受数据和关键字参数的绘图函数。它必须绘制到当前活动的 matplotlib 轴并采用`color`关键字参数。如果在`hue`维度上进行分面，必须也使用`label`关键字参数。 14 | 15 | `args`：字符串 16 | 17 | > 数据的列名，用于标识要绘制数据的变量。每个变量的数据按照调用中指定变量的顺序传递给`func`。 18 | 19 | `kwargs`：关键字参数 20 | 21 | > 所有的关键字参数都被传递给绘图函数。 22 | 23 | 24 | 返回值：`self`：对象 25 | 26 | > 返回自身。 27 | 28 | -------------------------------------------------------------------------------- /docs/34.md: -------------------------------------------------------------------------------- 1 | # seaborn.FacetGrid.map_dataframe 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | FacetGrid.map_dataframe(func, *args, **kwargs) 7 | ``` 8 | 9 | 和 `.map`类似，但是将 args 作为字符串传递并将数据插入到 kwargs 中. 10 | 11 | 此方法适用于使用接受长格式 DataFrame 作为`data`关键字参数并使用字符串变量名访问该 DataFrame 中的数据的函数进行绘图。 12 | 13 | 参数：`func`：可调用 14 | 15 | > 一个接受数据和关键字参数的绘图函数。与`map`方法不同,此处使用的函数必须“理解”Pandas 对象。它也必须绘制到当前活动的 matpltolib 轴并采用`color`关键字参数。如果在hue维度上进行分面，必须也使用label关键字参数。 16 | 17 | `args`：字符串 18 | 19 | > 数据的列名，用于标识要绘制数据的变量。每个变量的数据按照调用中指定变量的顺序传递给func。 20 | 21 | `kwargs`：关键字参数 22 | 23 | > 所有的关键字参数被传递给绘图函数。 24 | 25 | 26 | 返回值：`self`：对象 27 | 28 | > 返回自身。 29 | 30 | -------------------------------------------------------------------------------- /docs/36.md: -------------------------------------------------------------------------------- 1 | # seaborn.PairGrid.map 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | PairGrid.map(func, **kwargs) 7 | ``` 8 | 9 | 在每个子图中用相同的函数绘制。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 必须将 x,y 数组作为位置参数并绘制到“当前活动”的 matplotlib 轴上。还需要接受名为`color`和`label`的 kwargs。 14 | 15 | -------------------------------------------------------------------------------- /docs/37.md: -------------------------------------------------------------------------------- 1 | # seaborn.PairGrid.map_diag 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | PairGrid.map_diag(func, **kwargs) 7 | ``` 8 | 9 | 在每一个对角线子图上用一个单变量函数绘制。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | >必须将 x,y 数组作为位置参数并绘制到“当前活动”的 matplotlib 轴上。还需要接受名为 color 和 label 的 kwargs。 14 | 15 | -------------------------------------------------------------------------------- /docs/38.md: -------------------------------------------------------------------------------- 1 | # seaborn.PairGrid.map_offdiag 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | PairGrid.map_offdiag(func, **kwargs) 7 | ``` 8 | 9 | 在非对角线子图上用一个双变量函数绘图。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 必须将 x,y 数组作为位置参数并绘制到“当前活动”的 matplotlib 轴上。还需要接受名为 color 和 label 的 kwargs。 14 | 15 | -------------------------------------------------------------------------------- /docs/39.md: -------------------------------------------------------------------------------- 1 | # seaborn.PairGrid.map_lower 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | PairGrid.map_lower(func, **kwargs) 7 | ``` 8 | 9 | 在下对角线子图上用一个双变量函数绘图。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 必须将 x,y 数组作为位置参数并绘制到“当前活动”的 matplotlib 轴上。还需要接受名为`color`和`label`的 kwargs。 14 | 15 | -------------------------------------------------------------------------------- /docs/40.md: -------------------------------------------------------------------------------- 1 | # seaborn.PairGrid.map_upper 2 | 3 | > 译者：[sfw134](https://github.com/sfw134) 4 | 5 | ```py 6 | PairGrid.map_upper(func, **kwargs) 7 | ``` 8 | 9 | 在上对角线子图上用一个双变量函数绘图。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 必须将 x,y 数组作为位置参数并绘制到“当前活动”的 matplotlib 轴上。还需要接受名为 color 和 label 的 kwargs。 14 | 15 | -------------------------------------------------------------------------------- /docs/42.md: -------------------------------------------------------------------------------- 1 | # seaborn.JointGrid.plot 2 | 3 | > 译者：[Yet-sun](https://github.com/Yet-sun) 4 | 5 | ```py 6 | JointGrid.plot(joint_func, marginal_func, annot_func=None) 7 | ``` 8 | 9 | 绘制完整绘图的快捷方式。 10 | 11 | 直接使用 `plot_joint` 和 `plot_marginals` 进行更多控制. 12 | 13 | 参数：**joint_func, marginal_func：可调用** 14 | 15 | > 绘制双变量和单变量图的函数。 16 | 17 | 返回值：`self`：JointGrid 实例 18 | 19 | > 返回 `self`. 20 | -------------------------------------------------------------------------------- /docs/43.md: -------------------------------------------------------------------------------- 1 | # seaborn.JointGrid.plot_joint 2 | 3 | > 译者：[Yet-sun](https://github.com/Yet-sun) 4 | 5 | ```py 6 | JointGrid.plot_joint(func, **kwargs) 7 | ``` 8 | 9 | 绘制 `x` and `y`的双变量图。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 这必须将两个一维数据数组作为前两个位置参数，并且必须在“当前”轴上绘制。 14 | 15 | `kwargs`：键，值映射 16 | 17 | > 关键字参数传递给绘图函数。 18 | 19 | 返回值：`self`：JointGrid 实例 20 | 21 | > 返回 `self`. 22 | -------------------------------------------------------------------------------- /docs/44.md: -------------------------------------------------------------------------------- 1 | # seaborn.JointGrid.plot_marginals 2 | 3 | > 译者：[Yet-sun](https://github.com/Yet-sun) 4 | 5 | ```py 6 | JointGrid.plot_marginals(func, **kwargs) 7 | ``` 8 | 9 | 分别绘制 `x` 和 `y` 的单变量图。 10 | 11 | 参数：`func`：可调用的绘图函数 12 | 13 | > 这必须将一维数据数组作为第一个位置参数，它必须在“当前”轴上绘图，并且它必须接受“垂直”关键字参数以垂直定向图的度量维度。 14 | 15 | `kwargs`：键，值映射 16 | 17 | > 关键字参数传递给绘图函数。 18 | 19 | 返回值：`self`：JointGrid 实例 20 | 21 | > 返回 `self`. 22 | -------------------------------------------------------------------------------- /docs/45.md: -------------------------------------------------------------------------------- 1 | # seaborn.set 2 | 3 | > 译者：[lbllol365](https://github.com/lbllol365) 4 | 5 | ```python 6 | seaborn.set(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1, color_codes=True, rc=None) 7 | ``` 8 | 9 | 一步设定自定义图表参数。 10 | 11 | 每个参数可以被直接或者间接设定，参见下方的引用参数以获取更多信息。 12 | 13 | 参数：context：字符串或者字典 14 | 15 | > 绘图上下文参数，参见[`plotting_context()`](https://github.com/apachecn/seaborn-doc-zh/blob/master/docs/seaborn.plotting_context.html#seaborn.plotting_context) 16 | 17 | style：字符串或者字典 18 | 19 | > 坐标轴样式参数，参见[`axes_style()`](https://github.com/apachecn/seaborn-doc-zh/blob/master/docs/seaborn.axes_style.html#seaborn.axes_style) 20 | 21 | palette：字符串或者序列 22 | 23 | > 调色板，参见[`color_palette()`](https://github.com/apachecn/seaborn-doc-zh/blob/master/docs/seaborn.color_palette.html#seaborn.color_palette) 24 | 25 | font：字符串 26 | 27 | > 字体，参见 matplotlib 字体管理 28 | 29 | font_scale：浮点数，可选的 30 | 31 | > 独立缩放因子，以独立缩放字体元素的大小。 32 | 33 | color_codes：布尔值 34 | 35 | > 如果为真并且调色板是一个 seaborn 调色板，重新映射速记颜色代码（比如，"b"，"g"，"r"，等等）到调色板上的颜色。 36 | 37 | rc：字典或者 None 38 | 39 | > rc 参数字典映射以覆盖以上参数。 -------------------------------------------------------------------------------- /docs/46.md: -------------------------------------------------------------------------------- 1 | # seaborn.axes_style 2 | 3 | > 译者：[lbllol365](https://github.com/lbllol365) 4 | 5 | ```python 6 | seaborn.axes_style(style=None,rc=None) 7 | ``` 8 | 9 | 返回一个参数数组作为图表的自定义风格。 10 | 11 | 它会影响诸如坐标轴的颜色，是否默认启用网格，和其他自定义元素。 12 | 13 | 这个函数返回一个对象，该对象可以在 with 语句中使用以临时改变样式参数。 14 | 15 | 参数：style：字典,None,或者{darkgrid, whitegrid, dark, white, ticks}其中一个。 16 | 17 | > 一个参数字典或者一个预配置集的名称。 18 | 19 | rc：字典，可选的 20 | 21 | > 给定参数映射以覆盖预先设定的（默认的）seaborn 样式参数字典 22 | 23 | 参见 24 | 25 | 给一个 seaborn 主题设定 matplotlib 参数，返回一个参数字典，以缩放图标元素并可以定义图表的调色板。 26 | 27 | 例子： 28 | 29 | ```python 30 | >>> st = axes_style("whitegrid") 31 | ``` 32 | 33 | ```python 34 | >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) 35 | ``` 36 | 37 | ```python 38 | >>> import matplotlib.pyplot as plt 39 | >>> with axes_style("white"): 40 | ... f, ax = plt.subplots() 41 | ... ax.plot(x, y) 42 | ``` 43 | 44 | -------------------------------------------------------------------------------- /docs/47.md: -------------------------------------------------------------------------------- 1 | # seaborn.set_style 2 | 3 | > 译者：[lbllol365](https://github.com/lbllol365) 4 | 5 | ```python 6 | seaborn.set_style(style=None, rc=None) 7 | ``` 8 | 9 | 设定图表的自定义风格。 10 | 11 | 它会影响诸如坐标轴的颜色，网格默认是否开启和其他自定义元素。 12 | 13 | 参数：style：字典,None,或者{darkgrid, whitegrid, dark, white, ticks}其中一个。 14 | 15 | > 一个参数字典或者一个预配置集的名称。 16 | 17 | rc：字典，可选 18 | 19 | > 一个字典映射去覆盖 seaborn 样式字典中的预设值。这仅仅会更新那些被认为是样式定义一部分的参数。 20 | 21 | 参见 22 | 23 | 返回一个参数字典或者使用 with 语句以临时设置 style.set 的参数来缩放图表的元素或者设定图表的默认调色板。 24 | 25 | 例子： 26 | 27 | ```python 28 | >>> set_style("whitegrid") 29 | ``` 30 | 31 | ```python 32 | >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) 33 | ``` 34 | 35 | -------------------------------------------------------------------------------- /docs/48.md: -------------------------------------------------------------------------------- 1 | # seaborn.plotting_context 2 | 3 | > 译者：[cancan233](https://github.com/cancan233) 4 | 5 | ```python 6 | seaborn.plotting_context(context=None, font_scale=1, rc=None) 7 | ``` 8 | 以 dict 形式返回参数，用以缩放图形的元素。 9 | 10 | 这些参数可以影响诸如标签大小，线条和绘图的其他元素，但不会影响整体样式。基础文本时"notebook"，和其他文本"paper"，"talk"和"poster"，它们分别是 0.8，1.3 和 1.6 的 notebook 参数版本。 11 | 12 | 该函数返回一个对象，该对象可以在`with`语句中使用，临时改变文本参数。 13 | 14 | 参数：`context`：dict, None 或者是{paper, notebook, talk, poster}其中一个 15 | 16 | > 参数集或者是预设集合的名字 17 | 18 | `font_scale`：浮点数，可选 19 | 20 | > 单独的缩放因子可以独立缩放字体元素大小 21 | 22 | `rc`：dict，可选 23 | 24 | > 参数映射以覆盖预设的 seaborn 的文本字典中的值。这只更新被视为文本定义的一部分的参数。 25 | 26 | 也可参见 27 | 28 | 设置 matplotlib 参数以调整绘图元素返回定义图形样式的参数的 dict，定义绘图的调色板。 29 | 30 | 示例： 31 | 32 | ```python 33 | >>> c = plotting_context("poster") 34 | ``` 35 | 36 | ```python 37 | >>> c = plotting_context("notebook", font_scale=1.5) 38 | ``` 39 | 40 | ```python 41 | >>> c = plotting_context("talk", rc={"lines.linewidth": 2}) 42 | ``` 43 | 44 | ```python 45 | >>> import matplotlib.pyplot as plt 46 | >>> with plotting_context("paper"): 47 | ... f, ax = plt.subplots() 48 | ... ax.plot(x, y) 49 | ``` 50 | 51 | -------------------------------------------------------------------------------- /docs/49.md: -------------------------------------------------------------------------------- 1 | # seaborn.set_context 2 | 3 | > 译者：[cancan233](https://github.com/cancan233) 4 | 5 | ```python 6 | seaborn.set_context(context=None, font_scale=1, rc=None) 7 | ``` 8 | 9 | 设置绘图文本参数。 10 | 11 | 这些参数可以影响诸如标签大小，线条和绘图的其他元素，但不会影响整体样式。基础文本时"notebook"，和其他文本"paper"，"talk"和"poster"，它们分别是 0.8，1.3 和 1.6 的 notebook 参数版本。 12 | 13 | 参数：`context`：dict, None 或者是{paper, notebook, talk, poster}其中一个 14 | 15 | > 参数集或者是预设集合的名字 16 | 17 | `font_scale`：浮点数，可选 18 | 19 | > 单独的缩放因子可以独立缩放字体元素大小 20 | 21 | `rc`：dict，可选 22 | 23 | > 参数映射以覆盖预设的 seaborn 的上下文字典中的值。这只更新被视为上下文定义的一部分的参数。 24 | 25 | 也可参见 26 | 27 | 返回一个 rc 参数的字典，或者在`with`语句中使用临时设置 context.set 图样式的默认为数字的默认调色板 28 | 29 | 示例 30 | 31 | ```python 32 | >>> set_context("paper") 33 | ``` 34 | 35 | ```python 36 | >>> set_context("talk", font_scale=1.4) 37 | ``` 38 | 39 | ```python 40 | >>> set_context("talk", rc={"lines.linewidth": 2}) 41 | ``` 42 | 43 | -------------------------------------------------------------------------------- /docs/50.md: -------------------------------------------------------------------------------- 1 | # seaborn.set_color_codes 2 | 3 | > 译者：[cancan233](https://github.com/cancan233) 4 | 5 | ```python 6 | seaborn.set_color_codes(palette='deep') 7 | ``` 8 | 9 | 改变 matplotlib 颜色缩写词的解释方式。 10 | 11 | 调用此方法将改变 matplotlib 在后续图表中解释缩写词，例如"b"或"g"的方式。 12 | 13 | 参数： `palette`: {deep, muted, pastel, dark, bright, colorblind} 14 | 15 | > 预命名的 seaborn 调色板，用作颜色的来源 16 | 17 | 参见 18 | 19 | 可以通过高级 seaborn 样式管理器设置颜色代码。也可以通过设置 matplotlib 颜色循环功能设置颜色代码。 20 | 21 | 示例： 22 | 23 | 将 matplotlib 颜色代码映射到默认的 seaborn 调色板。 24 | 25 | ```python 26 | >>> import matplotlib.pyplot as plt 27 | >>> import seaborn as sns; sns.set() 28 | >>> sns.set_color_codes() 29 | >>> _ = plt.plot([0, 1], color="r") 30 | ``` 31 | 32 | ![http://seaborn.pydata.org/_images/seaborn-set_color_codes-1.png](img/f7331a33eaedf9eae59191642f7a64e4.jpg) 33 | 34 | 使用不同的 seaborn 调色板 35 | 36 | ```python 37 | >>> sns.set_color_codes("dark") 38 | >>> _ = plt.plot([0, 1], color="g") 39 | >>> _ = plt.plot([0, 2], color="m") 40 | ``` 41 | 42 | ![http://seaborn.pydata.org/_images/seaborn-set_color_codes-2.png](img/35544ecf6de962c91ab860527035f39f.jpg) 43 | 44 | -------------------------------------------------------------------------------- /docs/51.md: -------------------------------------------------------------------------------- 1 | # seaborn.reset_defaults 2 | 3 | > 译者：[cancan233](https://github.com/cancan233) 4 | 5 | ```python 6 | seaborn.reset_defaults() 7 | ``` 8 | 9 | 重置所有 RC 设置至默认设置。 -------------------------------------------------------------------------------- /docs/52.md: -------------------------------------------------------------------------------- 1 | # seaborn.reset_orig 2 | 3 | > 译者：[cancan233](https://github.com/cancan233) 4 | 5 | ```python 6 | seaborn.reset_orig() 7 | ``` 8 | 9 | 将所有 RC 参数恢复为最初设置(相对于自定义 rc 而言)。 10 | 11 | -------------------------------------------------------------------------------- /docs/53.md: -------------------------------------------------------------------------------- 1 | # seaborn.set_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.set_palette(palette, n_colors=None, desat=None, color_codes=False) 7 | ``` 8 | 9 | 通过 searborn 调色板设置 matplotlib 色彩循环 10 | 11 | 参数：`palette`：seaborn color paltte | matplotlib colormap | hls | husl 12 | 13 | > 调色板参数。 应该可以被 [`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette") 函数处理。 14 | 15 | `n_colors`：int 16 | 17 | > 色彩循环中的颜色数量。默认数量与`palette`模式有关, 查看[`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette")文档了解更多内容。 18 | 19 | `desat`：float 20 | 21 | > 每种颜色去饱和的比例。 22 | 23 | `color_codes`：bool 24 | 25 | > 如果为`True`，并且`palette`是 seaborn 调色板, 则将颜色代码简写 (例如“b”, “g”, “r”等等)映射到当前调色板上。 26 | 27 | 28 | 29 | 另外 30 | 31 | 在`with`语句中临时设置调色板或色彩循环。设置参数以调整绘图元素的默认参数。 32 | 33 | 例子 34 | 35 | ```py 36 | >>> set_palette("Reds") 37 | 38 | ``` 39 | 40 | ```py 41 | >>> set_palette("Set1", 8, .75) 42 | 43 | ``` 44 | -------------------------------------------------------------------------------- /docs/54.md: -------------------------------------------------------------------------------- 1 | # seaborn.color_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.color_palette(palette=None, n_colors=None, desat=None) 7 | ``` 8 | 9 | 返回一个颜色列表来定义一个调色板。 10 | 11 | ```py 12 | Available seaborn palette names: 13 | ``` 14 | 15 | 有 deep, muted, bright, pastel, dark, colorblind 六种颜色模式 16 | 17 | ```py 18 | Other options: 19 | ``` 20 | 21 | matplotlib Colormap 的名字、‘ch:’, ‘hls’, ‘husl’，或任一 matplotlib 接受的不同格式颜色列表。 22 | 23 | 调用此函数并设置 `palette=None` 会返回当前 matplotlib 色彩循环。 24 | 25 | matplotlib 调色板的顺序可以通过在调色板名称后添加 “_r” 来倒置，同样，添加 “_d” 可以将调色板设置为深色模式。（这些选项为互斥属性，返回的颜色列表同样可以被取反） 26 | 27 | 可以在 `with` 语句中使用此函数来为一个或多个点临时改变调色板。 28 | 29 | 参考这篇 [教程](http://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial) 来获取更多信息。 30 | 31 | 参数：**palette：None, string, or sequence, optional** 32 | 33 | > 调色板或者 None 值来返回给当前调色板。如果是序列，输入颜色会被使用，可能会被循环化并降低饱和度。 34 | 35 | 36 | `n_colors`：int, 可选 37 | 38 | > 调色板中的颜色数。如果为 None，则默认值将取决于调色板的指定方式。已命名调色板默认有 6 种颜色，抓取当前调色板或传递颜色列表不会更改颜色数，除非作出指定。要求比调色板中存在的颜色更多的颜色会导致调色板循环化。 39 | 40 | `desat`：float, 可选 41 | 42 | > 每种颜色的去饱和比例。 43 | 44 | 45 | 返回值：`palette`：RGB 元组序列。 46 | 47 | > 调色板。操作类似于列表，但可以用作上下文管理器，并具有转换为十六进制颜色代码的 `as_hex` 方法。 48 | 49 | 50 | 51 | 另外 52 | 53 | 设置所有的默认颜色循环。重新分配颜色代码，如 “b”、“g” 等。从 seaborn 调色板中选择颜色。 54 | 55 | 例子 56 | 57 | 不带参数的调用将返回当前默认颜色循环中的所有颜色： 58 | 59 | ```py 60 | >>> import seaborn as sns; sns.set() 61 | >>> sns.palplot(sns.color_palette()) 62 | 63 | ``` 64 | 65 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-1.png](img/9a5bb2b7fbd94af1db1d911dfbec1f7c.jpg) 66 | 67 | 显示另一个 “seaborn 调色板”，具有与默认 matplotlib 颜色循环相同的基本色调顺序，但颜色更吸引人。默认情况下，使用调色板名称进行调用将返回 6 种颜色： 68 | 69 | ```py 70 | >>> sns.palplot(sns.color_palette("muted")) 71 | 72 | ``` 73 | 74 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-2.png](img/f675cc14d11fb5585e5b1829cb29b569.jpg) 75 | 76 | 使用一个内置 matplotlib clolormap 的离散值： 77 | 78 | ```py 79 | >>> sns.palplot(sns.color_palette("RdBu", n_colors=7)) 80 | 81 | ``` 82 | 83 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-3.png](img/0aa3ba1dbc0e103e4d7b96c4de278ec9.jpg) 84 | 85 | 创建自定义 cubehelix 调色板： 86 | 87 | ```py 88 | >>> sns.palplot(sns.color_palette("ch:2.5,-.2,dark=.3")) 89 | 90 | ``` 91 | 92 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-4.png](img/6e3b5f94591e119cae1ead4b24d26c9e.jpg) 93 | 94 | 使用一个明确的 matplotlib 调色板并降低一些饱和度： 95 | 96 | ```py 97 | >>> sns.palplot(sns.color_palette("Set1", n_colors=8, desat=.5)) 98 | 99 | ``` 100 | 101 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-5.png](img/ce60a356e2dcd3cbe9cc67f1128c09c7.jpg) 102 | 103 | 创建 “dark”（深色）matplotlib 顺序调色板变体。(当对应于有序变量的多条线或点进行着色时，如果您不希望最轻的线不可见，则可以使用此选项)： 104 | 105 | ```py 106 | >>> sns.palplot(sns.color_palette("Blues_d")) 107 | 108 | ``` 109 | 110 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-6.png](img/fcc8ab8bf79674eb14cd31eead15dfd1.jpg) 111 | 112 | 作为上下文管理器使用： 113 | 114 | ```py 115 | >>> import numpy as np, matplotlib.pyplot as plt 116 | >>> with sns.color_palette("husl", 8): 117 | ... _ = plt.plot(np.c_[np.zeros(8), np.arange(8)].T) 118 | 119 | ``` 120 | 121 | ![http://seaborn.pydata.org/_images/seaborn-color_palette-7.png](img/5900496ed8560e0dbee0628adb29f5e9.jpg) 122 | -------------------------------------------------------------------------------- /docs/55.md: -------------------------------------------------------------------------------- 1 | # seaborn.husl_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.husl_palette(n_colors=6, h=0.01, s=0.9, l=0.65) 7 | ``` 8 | 9 | 在 HUSL 色调空间中获得一组均匀间隔的颜色。 10 | 11 | h, s, 和 l 值应该在 0 和 1 之间。 12 | 13 | 参数：`n_colors`：int 14 | 15 | > 调色板中的颜色数 16 | 17 | `h`：float 18 | 19 | > 第一个色调 20 | 21 | `s`：float 22 | 23 | > 饱和度 24 | 25 | `l`：float 26 | 27 | > 亮度 28 | 29 | 30 | 返回值：`palette`：seaborn 调色板 31 | 32 | > 类似列表的颜色对象的 RGB 元组。 33 | 34 | 35 | 36 | 另外 37 | 38 | 在 HSL 系统中使用等间距圆形色调创建一个调色板。 39 | 40 | 例子 41 | 42 | 使用默认参数创建一个有 10 种颜色的调色板： 43 | 44 | ```py 45 | >>> import seaborn as sns; sns.set() 46 | >>> sns.palplot(sns.husl_palette(10)) 47 | 48 | ``` 49 | 50 | ![http://seaborn.pydata.org/_images/seaborn-husl_palette-1.png](img/6c7a1ce9ff5aa3607ad3bf275bd012fc.jpg) 51 | 52 | 创建一个以不同色调值开头的 10 种颜色的调色板： 53 | 54 | ```py 55 | >>> sns.palplot(sns.husl_palette(10, h=.5)) 56 | 57 | ``` 58 | 59 | ![http://seaborn.pydata.org/_images/seaborn-husl_palette-2.png](img/e576eaf00adad68b231a4c2b8845d6e0.jpg) 60 | 61 | 创建一个比默认颜色更暗的 10 种颜色的调色板： 62 | 63 | ```py 64 | >>> sns.palplot(sns.husl_palette(10, l=.4)) 65 | 66 | ``` 67 | 68 | ![http://seaborn.pydata.org/_images/seaborn-husl_palette-3.png](img/752dc56ea71ef769a622efe0d5ea1b56.jpg) 69 | 70 | 创建 10 种颜色的调色板，其饱和度低于默认值： 71 | 72 | ```py 73 | >>> sns.palplot(sns.husl_palette(10, s=.4)) 74 | 75 | ``` 76 | 77 | ![http://seaborn.pydata.org/_images/seaborn-husl_palette-4.png](img/47c55edddae366e8d447c323d69b197f.jpg) 78 | -------------------------------------------------------------------------------- /docs/56.md: -------------------------------------------------------------------------------- 1 | # seaborn.hls_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.hls_palette(n_colors=6, h=0.01, l=0.6, s=0.65) 7 | ``` 8 | 9 | 在 HLS 色调空间中获取一组均匀间隔的颜色。 10 | 11 | h, s, 和 l 值应该在 0 和 1 之间。 12 | 13 | 参数：`n_colors`：int 14 | 15 | > 调色板中的颜色数 16 | 17 | `h`：float 18 | 19 | > 第一个色调 20 | 21 | `l`：float 22 | 23 | > 亮度 24 | 25 | `s`：float 26 | 27 | > 饱和度 28 | 29 | 30 | 返回值：`palette`：seaborn 调色板 31 | 32 | > 类似列表的颜色对象的 RGB 元组。 33 | 34 | 35 | 36 | 另外 37 | 38 | 在 HUSL 系统中使用等间距圆形色调创建一个调色板。 39 | 40 | 例子 41 | 42 | 使用默认参数创建一个有 10 种颜色的调色板： 43 | 44 | ```py 45 | >>> import seaborn as sns; sns.set() 46 | >>> sns.palplot(sns.hls_palette(10)) 47 | 48 | ``` 49 | 50 | ![http://seaborn.pydata.org/_images/seaborn-hls_palette-1.png](img/5147a9e7a6cd2453c2e80dd967724f7e.jpg) 51 | 52 | 创建一个以不同色调值开头的 10 种颜色的调色板： 53 | 54 | ```py 55 | >>> sns.palplot(sns.hls_palette(10, h=.5)) 56 | 57 | ``` 58 | 59 | ![http://seaborn.pydata.org/_images/seaborn-hls_palette-2.png](img/077b00661754439549e60127485a6a4a.jpg) 60 | 61 | 创建一个比默认颜色更暗的 10 种颜色的调色板： 62 | 63 | ```py 64 | >>> sns.palplot(sns.hls_palette(10, l=.4)) 65 | 66 | ``` 67 | 68 | ![http://seaborn.pydata.org/_images/seaborn-hls_palette-3.png](img/fdcac2831e87bea8b0a208095eb15adc.jpg) 69 | 70 | 创建 10 种颜色的调色板，其饱和度低于默认值： 71 | 72 | ```py 73 | >>> sns.palplot(sns.hls_palette(10, s=.4)) 74 | 75 | ``` 76 | 77 | ![http://seaborn.pydata.org/_images/seaborn-hls_palette-4.png](img/98715db5f9ee15781a7c49624ec8dafc.jpg) 78 | -------------------------------------------------------------------------------- /docs/57.md: -------------------------------------------------------------------------------- 1 | # seaborn.cubehelix_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.cubehelix_palette(n_colors=6, start=0, rot=0.4, gamma=1.0, hue=0.8, light=0.85, dark=0.15, reverse=False, as_cmap=False) 7 | ``` 8 | 9 | 用 cubehelix 系统制作顺序调色板。 10 | 11 | 生成亮度呈线性减小(或增大)的 colormap。这意味着 colormap 在转换为黑白模式时(用于打印)的信息将得到保留，且对色盲友好。“cubehelix” 也可以作为基于 matplotlib 的调色板使用，但此函数使用户可以更好地控制调色板的外观，并且具有一组不同的默认值。 12 | 13 | 除了使用这个函数，还可以在 seaborn 中使用字符串速记生成 cubehelix 调色板。 请参见下面的示例。 14 | 15 | 参数：`n_colors`：int 16 | 17 | > 调色板中的颜色数。 18 | 19 | `start`：float, 0 <= start <= 3 20 | 21 | > 第一个色调。 22 | 23 | `rot`：float 24 | 25 | > 围绕调色板范围内的色相控制盘旋转。 26 | 27 | `gamma`：float 0 <= gamma 28 | 29 | > Gamma 系数用以强调较深 (Gamma < 1) 或较浅 (Gamma > 1) 的颜色。 30 | 31 | `hue`：float, 0 <= hue <= 1 32 | 33 | > 颜色的饱和度。 34 | 35 | `dark`：float 0 <= dark <= 1 36 | 37 | > 调色板中最暗颜色的强度。 38 | 39 | `light`：float 0 <= light <= 1 40 | 41 | > 调色板中最浅颜色的强度。 42 | 43 | `reverse`：bool 44 | 45 | > 如果为 True 值，则调色板将从暗到亮。 46 | 47 | `as_cmap`：bool 48 | 49 | > 如果为 True 值，则返回 matplotlib colormap 而不是颜色列表。 50 | 51 | 52 | 返回值：`palette or cmap`：seaborn 调色板或者 matplotlib colormap 53 | 54 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 `as_cmap` 参数的值。 55 | 56 | 57 | 58 | 另外 59 | 60 | 启动交互式小部件以调整 cubehelix 调色板参数。创建具有暗低值的连续调色板。创建具有亮低值的连续调色板。 61 | 62 | 参考 63 | 64 | Green, D. A. (2011). “一种用于显示天文强度图像的配色方案”. Bulletin of the Astromical Society of India, Vol. 39, p. 289-295. 65 | 66 | 例子 67 | 68 | 生成默认调色板： 69 | 70 | ```py 71 | >>> import seaborn as sns; sns.set() 72 | >>> sns.palplot(sns.cubehelix_palette()) 73 | 74 | ``` 75 | 76 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-1.png](img/144ee4446ca7ff02dd03b831c6144dbb.jpg) 77 | 78 | 从相同的起始位置向后旋转： 79 | 80 | ```py 81 | >>> sns.palplot(sns.cubehelix_palette(rot=-.4)) 82 | 83 | ``` 84 | 85 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-2.png](img/e558f322ef6826a8b67e10ff25919759.jpg) 86 | 87 | 使用不同的起点和较短的旋转： 88 | 89 | ```py 90 | >>> sns.palplot(sns.cubehelix_palette(start=2.8, rot=.1)) 91 | 92 | ``` 93 | 94 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-3.png](img/664ee3a6a4dfcace7697e385779f371b.jpg) 95 | 96 | 反转亮度渐变方向： 97 | 98 | ```py 99 | >>> sns.palplot(sns.cubehelix_palette(reverse=True)) 100 | 101 | ``` 102 | 103 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-4.png](img/13804c3cf58c9d108ea1d638b643451f.jpg) 104 | 105 | 生成一个 colormap 对象： 106 | 107 | ```py 108 | >>> from numpy import arange 109 | >>> x = arange(25).reshape(5, 5) 110 | >>> cmap = sns.cubehelix_palette(as_cmap=True) 111 | >>> ax = sns.heatmap(x, cmap=cmap) 112 | 113 | ``` 114 | 115 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-5.png](img/17fa14f23873e6b75933971c609b4fe5.jpg) 116 | 117 | 使用完整的亮度范围： 118 | 119 | ```py 120 | >>> cmap = sns.cubehelix_palette(dark=0, light=1, as_cmap=True) 121 | >>> ax = sns.heatmap(x, cmap=cmap) 122 | 123 | ``` 124 | 125 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-6.png](img/ced91ab8985cc6cb41a2b85e098f14e4.jpg) 126 | 127 | 使用 [`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette") 函数接口： 128 | 129 | ```py 130 | >>> sns.palplot(sns.color_palette("ch:2,r=.2,l=.6")) 131 | 132 | ``` 133 | 134 | ![http://seaborn.pydata.org/_images/seaborn-cubehelix_palette-7.png](img/da5c0a4d6279edb54b0bac3bdfe7fed5.jpg) 135 | -------------------------------------------------------------------------------- /docs/58.md: -------------------------------------------------------------------------------- 1 | # seaborn.dark_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input='rgb') 7 | ``` 8 | 9 | 制作一个混合深色和 `color` 模式的顺序调色板。 10 | 11 | 这种调色板适用于数据集的范围从相对低值(不感兴趣)到相对高值(很感兴趣)时。 12 | 13 | 可以通过多种方式指定 `color` 参数，包括用于在 matplotlib 中定义颜色的所有选项，以及由 seborn 处理的其他几个颜色空间。也可以使用 XKCD color survey 中的颜色名字数据库。 14 | 15 | 如果您在使用 IPython notebook，您还可以通过 [`choose_dark_palette()`](seaborn.choose_dark_palette.html#seaborn.choose_dark_palette "seaborn.choose_dark_palette") 函数交互式选择调色板。 16 | 17 | 参数：`color`：高值的基色 18 | 19 | > 十六进制、RGB 元组或者颜色名字。 20 | 21 | `n_colors`：int, 可选 22 | 23 | > 调色板中的颜色数。 24 | 25 | `reverse`：bool, 可选 26 | 27 | > 如果为 True 值，则反转混合的方向。 28 | 29 | `as_cmap`：bool, optional 30 | 31 | > 如果为 True 值，则返回 matplotlib colormap 而不是列表。 32 | 33 | `input`：{‘rgb’, ‘hls’, ‘husl’, xkcd’} 34 | 35 | > 用于解释输入颜色的颜色空间。前三个选项适用于元组输入，后者适用于字符串输入。 36 | 37 | 38 | 返回值：`palette or cmap`：seaborn color palette or matplotlib colormap 39 | 40 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 as_cmap 参数的值。 41 | 42 | 43 | 44 | 另外 45 | 46 | 创建具有暗低值的连续调色板。创建有两种颜色的发散调色板。 47 | 48 | 例子 49 | 50 | 从一个 HTML 颜色生成一个调色板： 51 | 52 | ```py 53 | >>> import seaborn as sns; sns.set() 54 | >>> sns.palplot(sns.dark_palette("purple")) 55 | 56 | ``` 57 | 58 | ![http://seaborn.pydata.org/_images/seaborn-dark_palette-1.png](img/d206a78223b0280bfffbfd7cdb262145.jpg) 59 | 60 | 生成亮度降低的调色板： 61 | 62 | ```py 63 | >>> sns.palplot(sns.dark_palette("seagreen", reverse=True)) 64 | 65 | ``` 66 | 67 | ![http://seaborn.pydata.org/_images/seaborn-dark_palette-2.png](img/eac4541fd6a3e7b30eb535e0a1aab99f.jpg) 68 | 69 | 从 HUSL 空间种子生成选项板： 70 | 71 | ```py 72 | >>> sns.palplot(sns.dark_palette((260, 75, 60), input="husl")) 73 | 74 | ``` 75 | 76 | ![http://seaborn.pydata.org/_images/seaborn-dark_palette-3.png](img/6e81b4c302189f59de63f96dbee3a197.jpg) 77 | 78 | 生成一个 colormap 对象： 79 | 80 | ```py 81 | >>> from numpy import arange 82 | >>> x = arange(25).reshape(5, 5) 83 | >>> cmap = sns.dark_palette("#2ecc71", as_cmap=True) 84 | >>> ax = sns.heatmap(x, cmap=cmap) 85 | 86 | ``` 87 | 88 | ![http://seaborn.pydata.org/_images/seaborn-dark_palette-4.png](img/f153ef2783202a95337d9ea723fe2984.jpg) 89 | -------------------------------------------------------------------------------- /docs/59.md: -------------------------------------------------------------------------------- 1 | # seaborn.light_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.light_palette(color, n_colors=6, reverse=False, as_cmap=False, input='rgb') 7 | ``` 8 | 9 | 制作一个混合浅色和 `color` 模式的顺序调色板。 10 | 11 | 这种调色板适用于数据集的范围从相对低值(不感兴趣)到相对高值(很感兴趣)时。 12 | 13 | 可以通过多种方式指定 `color` 参数，包括用于在 matplotlib 中定义颜色的所有选项，以及由 seborn 处理的其他几个颜色空间。也可以使用 XKCD color survey 中的颜色名字数据库。 14 | 15 | 如果您在使用 IPython notebook，您还可以通过 [`choose_light_palette()`](seaborn.choose_light_palette.html#seaborn.choose_light_palette "seaborn.choose_light_palette") 函数交互式选择调色板。 16 | 17 | 参数：`color`：高值的基色 18 | 19 | > 十六进制、`input` 中的元组或者颜色名字。 20 | 21 | `n_colors`：int, 可选 22 | 23 | > 调色板中的颜色数。 24 | 25 | `reverse`：bool, 可选 26 | 27 | > 如果为 True 值，则反转混合的方向。 28 | 29 | `as_cmap`：bool, 可选 30 | 31 | > 如果为 True 值，则返回 matplotlib colormap 而不是列表。 32 | 33 | `input`：{‘rgb’, ‘hls’, ‘husl’, xkcd’} 34 | 35 | > 用于解释输入颜色的颜色空间。前三个选项适用于元组输入，后者适用于字符串输入。 36 | 37 | 返回值：`palette or cmap`：seaborn color palette or matplotlib colormap 38 | 39 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 as_cmap 参数的值。 40 | 41 | 42 | 43 | 另外 44 | 45 | 创建具有暗低值的连续调色板。创建有两种颜色的发散调色板。 46 | 47 | 例子 48 | 49 | 从一个 HTML 颜色生成一个调色板： 50 | 51 | ```py 52 | >>> import seaborn as sns; sns.set() 53 | >>> sns.palplot(sns.light_palette("purple")) 54 | 55 | ``` 56 | 57 | ![http://seaborn.pydata.org/_images/seaborn-light_palette-1.png](img/74a27bb78efdcf1f93f6c4c77035bb88.jpg) 58 | 59 | 生成亮度降低的调色板： 60 | 61 | ```py 62 | >>> sns.palplot(sns.light_palette("seagreen", reverse=True)) 63 | 64 | ``` 65 | 66 | ![http://seaborn.pydata.org/_images/seaborn-light_palette-2.png](img/1b8a02d12771dd98fcd113bf3a601e27.jpg) 67 | 68 | 从 HUSL 空间种子生成选项板： 69 | 70 | ```py 71 | >>> sns.palplot(sns.light_palette((260, 75, 60), input="husl")) 72 | 73 | ``` 74 | 75 | ![http://seaborn.pydata.org/_images/seaborn-light_palette-3.png](img/2d899c79eea5f0341a72b193b32ee84b.jpg) 76 | 77 | 生成一个 colormap 对象： 78 | 79 | ```py 80 | >>> from numpy import arange 81 | >>> x = arange(25).reshape(5, 5) 82 | >>> cmap = sns.light_palette("#2ecc71", as_cmap=True) 83 | >>> ax = sns.heatmap(x, cmap=cmap) 84 | 85 | ``` 86 | 87 | ![http://seaborn.pydata.org/_images/seaborn-light_palette-4.png](img/dc9db2a17529e652fe4c5a187892a2eb.jpg) 88 | -------------------------------------------------------------------------------- /docs/60.md: -------------------------------------------------------------------------------- 1 | # seaborn.diverging_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.diverging_palette(h_neg, h_pos, s=75, l=50, sep=10, n=6, center='light', as_cmap=False) 7 | ``` 8 | 9 | 在两个 HUSL 颜色直接建立一个发散调色板。 10 | 11 | 如果您在使用 IPython notebook，您还可以通过 [`choose_diverging_palette()`](seaborn.choose_diverging_palette.html#seaborn.choose_diverging_palette "seaborn.choose_diverging_palette") 函数交互式选择调色板。 12 | 13 | 参数：`h_neg, h_pos`：float in [0, 359] 14 | 15 | > 图的正负范围的锚定色调 16 | 17 | `s`：[0, 100] 范围内的浮点数，可选 18 | 19 | > 图的两个范围的锚定饱和度 20 | 21 | `l`：[0, 100] 范围内的浮点数，可选 22 | 23 | > 图的两个范围的锚定亮度 24 | 25 | `n`：int，可选 26 | 27 | > 调色板中的颜色数（如果为 not，返回一个 colormap） 28 | 29 | `center`：{“light”, “dark”}, 可选 30 | 31 | > 调色板中心为亮或暗 32 | 33 | `as_cmap`：bool, 可选 34 | 35 | > 如果为 true，返回一个 matplotlib colormap 而不是一个颜色列表。 36 | 37 | 38 | 返回值：`palette or cmap`：seaborn color palette or matplotlib colormap 39 | 40 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 `as_cmap` 参数的值。 41 | 42 | 43 | 44 | 45 | 另外 46 | 47 | 创建具有暗值的连续调色板。创建具有亮值的连续调色板。 48 | 49 | 例子 50 | 51 | 生成一个蓝-白-红调色板： 52 | 53 | ```py 54 | >>> import seaborn as sns; sns.set() 55 | >>> sns.palplot(sns.diverging_palette(240, 10, n=9)) 56 | 57 | ``` 58 | 59 | ![http://seaborn.pydata.org/_images/seaborn-diverging_palette-1.png](img/35c6cb5f121884b387bc70e68ed2a22d.jpg) 60 | 61 | 生成一个更亮的绿-白-紫调色板： 62 | 63 | ```py 64 | >>> sns.palplot(sns.diverging_palette(150, 275, s=80, l=55, n=9)) 65 | 66 | ``` 67 | 68 | ![http://seaborn.pydata.org/_images/seaborn-diverging_palette-2.png](img/bc461a32c1b91013ce1dbb7e3a78d94c.jpg) 69 | 70 | 生成一个蓝-黑-红调色板: 71 | 72 | ```py 73 | >>> sns.palplot(sns.diverging_palette(250, 15, s=75, l=40, 74 | ... n=9, center="dark")) 75 | 76 | ``` 77 | 78 | ![http://seaborn.pydata.org/_images/seaborn-diverging_palette-3.png](img/9003952e6b318f40954e978ce39dd033.jpg) 79 | 80 | 生成一个 colormap 对象: 81 | 82 | ```py 83 | >>> from numpy import arange 84 | >>> x = arange(25).reshape(5, 5) 85 | >>> cmap = sns.diverging_palette(220, 20, sep=20, as_cmap=True) 86 | >>> ax = sns.heatmap(x, cmap=cmap) 87 | 88 | ``` 89 | 90 | ![http://seaborn.pydata.org/_images/seaborn-diverging_palette-4.png](img/f0eb41dc444960d4e07f9de19e5e8568.jpg) 91 | -------------------------------------------------------------------------------- /docs/61.md: -------------------------------------------------------------------------------- 1 | # seaborn.blend_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.blend_palette(colors, n_colors=6, as_cmap=False, input='rgb') 7 | ``` 8 | 9 | 创建在颜色列表之间混合的调色板。 10 | 11 | 参数：`colors`：由 `input` 解释的各种格式的颜色序列。 12 | 13 | > 十六进制码，html 颜色名字，或者 `input` 空间中的元组。 14 | 15 | `n_colors`：int, 可选 16 | 17 | > 调色板中的颜色数。 18 | 19 | `as_cmap`：bool, 可选 20 | 21 | > 如果为 True 值，则返回 matplotlib colormap 而不是列表。 22 | 23 | 24 | 返回值：`palette or cmap`：seaborn 调色板或 matplotlib colormap 25 | 26 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 `as_cmap` 参数的值。 27 | 28 | -------------------------------------------------------------------------------- /docs/62.md: -------------------------------------------------------------------------------- 1 | # seaborn.xkcd_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.xkcd_palette(colors) 7 | ``` 8 | 9 | 使用来自 xkcd color survey 的颜色名字生成调色板。 10 | 11 | 查看完整的 xkcd 颜色列表： [https://xkcd.com/color/rgb/](https://xkcd.com/color/rgb/) 12 | 13 | 这是一个简单的 `seaborn.xkcd_rgb` 字典的装饰器。 14 | 15 | 参数：`colors`：字符串列表 16 | 17 | > `seaborn.xkcd_rgb` 字典中的 key 的列表。 18 | 19 | 20 | 返回值：`palette`：seaborn 调色板 21 | 22 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 `as_cmap` 参数的值。 23 | 24 | 25 | 26 | 另外 27 | 28 | 使用 Crayola crayon colors 创建调色板。 -------------------------------------------------------------------------------- /docs/63.md: -------------------------------------------------------------------------------- 1 | # seaborn.crayon_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.crayon_palette(colors) 7 | ``` 8 | 9 | 通过 Crayola crayons 颜色名字生成调色板。 10 | 11 | 颜色列表在这里获取： [https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors](https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors) 12 | 13 | 这是一个简单的 `seaborn.crayons` 字典的装饰器。 14 | 15 | 参数：`colors`：字符串列表 16 | 17 | > List of keys in the `seaborn.crayons` dictionary. 18 | 19 | 20 | 返回值：`palette`：seaborn 调色板 21 | 22 | > Returns the list of colors as rgb tuples in an object that behaves like other seaborn color palettes.返回一个类似 serborn 调色板的对象，其中包括 RGB 元组构成的的颜色列表。 23 | 24 | 25 | 26 | 另外 27 | 28 | 使用来自 xkcd color survey 的颜色名字创建调色板。 29 | -------------------------------------------------------------------------------- /docs/64.md: -------------------------------------------------------------------------------- 1 | # seaborn.mpl_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.mpl_palette(name, n_colors=6) 7 | ``` 8 | 9 | 从一个 matplotlib 调色板中返回离散颜色。 10 | 11 | 请注意，这会正确处理定性的 colorbrewer 调色板，但如果您要求的颜色多于特定的定性调色板，提供的颜色将会比您预期的少。相反，使用 [`color_palette()`](seaborn.color_palette.html#seaborn.color_palette "seaborn.color_palette") 函数请求一个定性 colorbrewer 调色板将会返回预期数目的颜色，但是是循环型的。 12 | 13 | 如果您在使用 IPython notebook，您还可以通过 [`choose_colorbrewer_palette()`](seaborn.choose_colorbrewer_palette.html#seaborn.choose_colorbrewer_palette "seaborn.choose_colorbrewer_palette") 函数交互式选择调色板。 14 | 15 | 参数：`name`：string 16 | 17 | > 调色板名字，应该是一个被命名的 matplotlib colormap。 18 | 19 | `n_colors`：int 20 | 21 | > 调色板中离散颜色的个数。 22 | 23 | 24 | 返回值：`palette or cmap`：seaborn 调色板或者 matplotlib colormap 25 | 26 | > 类似列表的颜色对象的 RGB 元组，或者可以将连续值映射到颜色的 colormap 对象，具体取决于 `as_cmap` 参数的值。 27 | 28 | 例子 29 | 30 | 生成一个含有 8 种颜色的定性 colorbrewer 调色板： 31 | 32 | ```py 33 | >>> import seaborn as sns; sns.set() 34 | >>> sns.palplot(sns.mpl_palette("Set2", 8)) 35 | 36 | ``` 37 | 38 | ![http://seaborn.pydata.org/_images/seaborn-mpl_palette-1.png](img/df633411e4b51b4749de8c5349b438cc.jpg) 39 | 40 | 生成一个连续的 colorbrewer 调色板： 41 | 42 | ```py 43 | >>> sns.palplot(sns.mpl_palette("Blues")) 44 | 45 | ``` 46 | 47 | ![http://seaborn.pydata.org/_images/seaborn-mpl_palette-2.png](img/e369a8cb7348d971f2471d297addf456.jpg) 48 | 49 | 生成一个发散调色板： 50 | 51 | ```py 52 | >>> sns.palplot(sns.mpl_palette("seismic", 8)) 53 | 54 | ``` 55 | 56 | ![http://seaborn.pydata.org/_images/seaborn-mpl_palette-3.png](img/2690d286a343ff53a43d15d54fc86e91.jpg) 57 | 58 | 生成一个 “dark” 顺序调色板： 59 | 60 | ```py 61 | >>> sns.palplot(sns.mpl_palette("GnBu_d")) 62 | 63 | ``` 64 | 65 | ![http://seaborn.pydata.org/_images/seaborn-mpl_palette-4.png](img/dc21c66a962fb741f9f8f63c0374fae1.jpg) 66 | -------------------------------------------------------------------------------- /docs/65.md: -------------------------------------------------------------------------------- 1 | # seaborn.choose_colorbrewer_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.choose_colorbrewer_palette(data_type, as_cmap=False) 7 | ``` 8 | 9 | 在 ColorBrewer 集中选择一个调色板。 10 | 11 | 这些调色板内置于 matplotlib 库中，可以通过名字在 seaborn 函数中调用，或者作为对象返回给这个函数。 12 | 13 | 参数：`data_type`：{‘sequential’, ‘diverging’, ‘qualitative’} 14 | 15 | > 描述您想要实现可视化的数据类型。查看 serborn 调色板文档来了解更多如何取值。注意，您可以传递子字符串（例如，‘q’ 代表 ‘qualitative‘） 16 | 17 | `as_cmap`：bool 18 | 19 | > 如果为 True 值，则返回 matplotlib colormap 而不是离散颜色的列表。 20 | 21 | 返回值：`pal or cmap`：颜色列表或 matplotlib colormap 22 | 23 | > 可以被传递给 plotting 函数的对象。 24 | 25 | 26 | 27 | 另外 28 | 29 | 创建具有暗低值的连续调色板。创建具有亮低值的连续调色板。从选中的颜色中创建发散调色板。使用 cubehelix 创建连续调色板或者 colormap。 30 | -------------------------------------------------------------------------------- /docs/66.md: -------------------------------------------------------------------------------- 1 | # seaborn.choose_cubehelix_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.choose_cubehelix_palette(as_cmap=False) 7 | ``` 8 | 9 | 启动交互式小部件以创建顺序 cubehelix 调色板。 10 | 11 | 这与 [`cubehelix_palette()`](seaborn.cubehelix_palette.html#seaborn.cubehelix_palette "seaborn.cubehelix_palette") 函数相对应。这种调色板在数据集的范围从相对低值(不感兴趣)到相对高值(很感兴趣)时很有用。cubehelix 系统允许调色板在整个范围内具有更多的色调变化，这有助于区分更广泛的值。 12 | 13 | 需要 2.0 以上版本 IPython，必须在 notebook 中使用。 14 | 15 | 参数：`as_cmap`：bool 16 | 17 | > 如果为 True 值，则返回 matplotlib colormap 而不是离散颜色的列表。 18 | 19 | 20 | 返回值：`pal or cmap`：颜色列表或 matplotlib colormap 21 | 22 | > 可以被传递给 plotting 函数的对象。 23 | 24 | 另外 25 | 26 | 使用 cubehelix 创建连续调色板或者 colormap。 27 | -------------------------------------------------------------------------------- /docs/67.md: -------------------------------------------------------------------------------- 1 | # seaborn.choose_light_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.choose_light_palette(input='husl', as_cmap=False) 7 | ``` 8 | 9 | 启动交互式小部件以创建亮色顺序调色板。 10 | 11 | 与 [`light_palette()`](seaborn.light_palette.html#seaborn.light_palette "seaborn.light_palette") 函数相对应。 这种调色板在数据集的范围从相对低值(不感兴趣)到相对高值(很感兴趣)时很有用。 12 | 13 | 需要 2.0 以上版本 IPython，必须在 notebook 中使用。 14 | 15 | 参数：`input`：{‘husl’, ‘hls’, ‘rgb’} 16 | 17 | > 色彩空间用于定义种子值。请注意，此默认值与 [`light_palette()`](seaborn.light_palette.html#seaborn.light_palette "seaborn.light_palette") 的默认输入值不同。 18 | 19 | 参数：`as_cmap`：bool 20 | 21 | > 如果为 True 值，则返回 matplotlib colormap 而不是离散颜色的列表。 22 | 23 | 24 | 返回值：`pal or cmap`：颜色列表或 matplotlib colormap 25 | 26 | > 可以被传递给 plotting 函数的对象。 27 | 28 | 另外 29 | 30 | 创建具有暗低值的连续调色板。创建具有亮低值的连续调色板。使用 cubehelix 创建连续调色板或者 colormap。 31 | -------------------------------------------------------------------------------- /docs/68.md: -------------------------------------------------------------------------------- 1 | # seaborn.choose_dark_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.choose_dark_palette(input='husl', as_cmap=False) 7 | ``` 8 | 9 | 启动交互式小部件以创建暗色顺序调色板。 10 | 11 | 与 [`dark_palette()`](seaborn.dark_palette.html#seaborn.dark_palette "seaborn.dark_palette") 函数相对应。这种调色板在数据集的范围从相对低值(不感兴趣)到相对高值(很感兴趣)时很有用。 12 | 13 | 需要 2.0 以上版本 IPython，必须在 notebook 中使用。 14 | 15 | 参数：`input`：{‘husl’, ‘hls’, ‘rgb’} 16 | 17 | > 色彩空间用于定义种子值。请注意，此默认值与 [`dark_palette()`](seaborn.dark_palette.html#seaborn.dark_palette "seaborn.dark_palette") 的默认输入值不同。 18 | 19 | 20 | 参数：`as_cmap`：bool 21 | 22 | > 如果为 True 值，则返回 matplotlib colormap 而不是离散颜色的列表。 23 | 24 | 25 | 返回值：`pal or cmap`：颜色列表或 matplotlib colormap 26 | 27 | > 可以被传递给 plotting 函数的对象。 28 | 29 | 另外 30 | 31 | 创建具有暗低值的连续调色板。创建具有亮低值的连续调色板。使用 cubehelix 创建连续调色板或者 colormap。 32 | -------------------------------------------------------------------------------- /docs/69.md: -------------------------------------------------------------------------------- 1 | # seaborn.choose_diverging_palette 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.choose_diverging_palette(as_cmap=False) 7 | ``` 8 | 9 | 启动交互式小部件以创建一个发散调色板。 10 | 11 | 这与 [`diverging_palette()`](seaborn.diverging_palette.html#seaborn.diverging_palette "seaborn.diverging_palette") 函数相对应。这种调色板适用于对低值和高值都感兴趣并且中间点也有意义的数据。（例如，相对与某些基准值的分数变化）。 12 | 13 | 需要 2.0 以上版本 IPython，必须在 notebook 中使用。 14 | 15 | 参数：`as_cmap`：bool 16 | 17 | > 如果为 True 值，则返回 matplotlib colormap 而不是离散颜色的列表。 18 | 19 | 20 | 返回值：`pal or cmap`：颜色列表或 matplotlib colormap 21 | 22 | > 可以被传递给 plotting 函数的对象。 23 | 24 | 另外 25 | 26 | 生成一个发散调色板或者 colormap。从 colorbrewer 集中交互式选择调色板，包括发散调色板。 27 | -------------------------------------------------------------------------------- /docs/70.md: -------------------------------------------------------------------------------- 1 | # seaborn.load_dataset 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.load_dataset(name, cache=True, data_home=None, **kws) 7 | ``` 8 | 9 | 从在线库中获取数据集（需要联网）。 10 | 11 | 参数：`name`：字符串 12 | 13 | > 数据集的名字 (`name`.csv on [https://github.com/mwaskom/seaborn-data](https://github.com/mwaskom/seaborn-data))。 您可以通过 `get_dataset_names()` 获取可用的数据集。 14 | 15 | `cache`：boolean, 可选 16 | 17 | > 如果为 True，则在本地缓存数据并在后续调用中使用缓存。 18 | 19 | `data_home`：string, 可选 20 | 21 | > 用于存储缓存数据的目录。 默认情况下使用 ~/seaborn-data/ 22 | 23 | `kws`：dict, 可选 24 | 25 | > 传递给 pandas.read_csv 26 | 27 | -------------------------------------------------------------------------------- /docs/71.md: -------------------------------------------------------------------------------- 1 | # seaborn.despine 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False, offset=None, trim=False) 7 | ``` 8 | 9 | 从图中移除顶部和右侧脊柱。 10 | 11 | ```py 12 | fig : matplotlib 值, 可选 13 | ``` 14 | 15 | 去除所有轴脊柱，默认使用当前数值。 16 | 17 | ```py 18 | ax : matplotlib 轴, 可选 19 | ``` 20 | 21 | 去除特定的轴脊柱。 22 | 23 | ```py 24 | top, right, left, bottom : boolean, 可选 25 | ``` 26 | 27 | 如果为 True，去除脊柱。 28 | 29 | ```py 30 | offset : int or dict, 可选 31 | ``` 32 | 33 | 绝对距离（以磅为单位）应将脊椎移离轴线（负值向内移动脊柱）。 单个值适用于所有脊柱; 字典可用于设置每侧的偏移值。 34 | 35 | ```py 36 | trim : bool, 可选 37 | ``` 38 | 39 | 如果为 True，则将脊柱限制为每个非去除脊柱的轴上的最小和最大主刻度。 40 | 41 | 返回值：None | 42 | | --- | --- | 43 | -------------------------------------------------------------------------------- /docs/72.md: -------------------------------------------------------------------------------- 1 | # seaborn.desaturate 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.desaturate(color, prop) 7 | ``` 8 | 9 | 减少颜色的饱和度。 10 | 11 | 参数：`color`：matplotlib 颜色 12 | 13 | > 十六进制，rgb 元组或者 html 颜色名字 14 | 15 | `prop`：float 16 | 17 | > 颜色的饱和度将乘以该值 18 | 19 | 返回值：`new_color`：rgb 元组 20 | 21 | > RGB 元组表示去饱和的颜色代码 22 | 23 | -------------------------------------------------------------------------------- /docs/73.md: -------------------------------------------------------------------------------- 1 | # seaborn.saturate 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.saturate(color) 7 | ``` 8 | 9 | 返回具有相同色调的完全饱和的颜色。 10 | 11 | 参数：`color`：matplotlib 颜色 12 | 13 | > 十六进制，rgb 元组或者 html 颜色名字 14 | 15 | 16 | 返回值：`new_color`：rgb 元组 17 | 18 | > RGB 元组表示的饱和颜色代码 19 | 20 | -------------------------------------------------------------------------------- /docs/74.md: -------------------------------------------------------------------------------- 1 | # seaborn.set_hls_values 2 | 3 | > 译者：[Modrisco](https://github.com/Modrisco) 4 | 5 | ```py 6 | seaborn.set_hls_values(color, h=None, l=None, s=None) 7 | ``` 8 | 9 | 独立修改一个颜色的 h, l 或 s 值。 10 | 11 | 参数：`color`：matplotlib 颜色 12 | 13 | > 十六进制，rgb 元组或者 html 颜色名字 14 | 15 | `h, l, s`：0 到 1 之间的浮点数，或者为 None 16 | 17 | > hls 空间中的新值。 18 | 19 | 20 | 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